South Asians residing in the United States have lower physical activity levels than other ethnicities and show decreases in activity levels with longer residence in the United States (1,2). U.S. South Asian immigrants also have a 1.5–2 times higher prevalence of cardiometabolic diseases like coronary heart disease and type 2 diabetes than other race/ethnic groups (3,4). Further, South Asians experience cardiometabolic diseases at younger ages (5,6) and experience a lower body mass index (BMI) threshold for cardiometabolic disease than those of European decent, where disease-related symptoms begin as low as 21 kg·m−2, compared with 30 kg·m−2 in European decent (7,8). An increasing sedentary lifestyle, increased abdominal fat, and poor diet after immigration are suggested drivers for these cardiometabolic health disparities (5,6).
Little is known about the exercise patterns and perceptions among South Asian adults in the United States. The Mediators of Atherosclerosis in South Asians Living in America study found a correlation between strong traditional cultural beliefs and lower compliance with the American Heart Association’s Life Simple 7 health metrics, with 65% meeting the physical activity guidelines (9). The objective of this article is to describe exercise patterns and behaviors among a migrant South Asian population living in the southern United States.
Design and Sample
The South Asian Health and Prevention Education (SHAPE) pilot study (clinicaltrials.gov no. NCT01084928) was conducted to design and test the feasibility of conducting a culturally tailored lifestyle change education diabetes prevention program for overweight, adult South Asian Americans with prediabetes in the United States. The analysis presented here includes data collected at a clinic-based testing visit conducted to identify eligible participants for the lifestyle intervention trial. Potential participants were recruited through advertisements in local South Asian magazines, information sent through community organization listservs, and in-person outreach at health fairs and screening, diabetes information events, and South Asian stores. In-person or phone-based prescreening (n = 117) included a brief screening questionnaire with demographic questions, the Finnish Diabetes Risk Score (FINDRISC) screening tool (10), at in-person assessments, and anthropometric measures. The FINDRISC uses age, sex, BMI, use of blood pressure medication, history of high glucose, physical activity, consumption of fruits and vegetables, and family history of diabetes to determine risk for type 2 diabetes. South Asian adults with an FINDRISC score of 11 or greater were invited for clinic-based screening. The Emory Institutional Review Board approved the study (protocol IRB00035893), and individuals provided written, informed consent before testing.
Testing was conducted at the Emory University Hospital Clinical Research Site, which is part of the Georgia Clinical and Translation Science Alliance network. The testing visit included a health questionnaire with sociodemographic, economic, behavioral, and psychosocial measures, anthropometric measurements, and clinical testing. A fasting blood sample was taken after an overnight fast of at least 8 h and was analyzed to assess plasma lipids (total cholesterol, LDL, HDL, and trigycerides), fasting glucose, fasting insulin, high-sensitivity C-reactive protein, glycated hemoglobin, complete blood count, and blood chemistry using Clinical Research Site protocols. After the fasting blood draw, a 75-g oral glucose tolerance test was performed, with blood draws at baseline, 30 min, and 2 h. An automated blood pressure monitor was used to measure blood pressure and resting heart rate in duplicate while the participant was seated and relaxed.
This analysis includes data from the participant’s responses to an exercise benefits and barriers scale and exercise self-efficacy scale, information on exercise frequency and habits, anthropometric measurements, and health characteristics. There were 55 participants who participated in clinic-based screening for the SHAPE study. Three adults were excluded from the analysis as they did not provide information about their sociodemographic characteristics and exercise patterns. The final analytic sample was 52 participants.
Demographic variables were categorized as follows: education as up to 2-yr college degree, 4-yr college degree (e.g., BA, BS), or graduate degree; household income as <$39,999, $40,000–99,999, or ≥100,000; marital status as married or not married; occupation as employed or other; and tobacco use as current, former, or never. Height was measured without shoes using a stadiometer and weight was measured on a digital scale with the participant in light clothing. BMI was calculated as the weight in kilograms divided by the height in squared meters and categorized as underweight (BMI < 18.5 kg·m−2), normal weight (BMI = 18.5–22.9 kg·m−2), overweight (23–27.5 kg·m−2), and obese (BMI ≥ 27.5 kg·m−2) using Asian-specific recommendations (11).
Exercise patterns were assessed by the following questions on the frequency, duration, type, and location of exercise: How many days per week do you exercise? On average how long does each exercise session last? What types of exercise do you do? Where do you exercise? The above questions were used to calculate the minutes of exercise per week for each participant. This variable was calculated by multiplying reported days of exercise with the reported duration/intensity of reported exercise. The U.S. Physical Activity Guidelines recommend at least 150 min of moderate to vigorous exercise per week (12). High exercisers were defined as individuals who reported exercising for at least 150 min·wk−1, whereas low exercisers were defined as individuals who reported exercising less than 150 min·wk−1. Type of exercise was categorized as walking, yoga, running/jogging, weightlifting, and other. Exercise location was collapsed as 1) home, 2) neighborhood and park, 3) gym/fitness club and community center, and 4) other.
Participants evaluated the perceived benefits and barriers to exercise using a 43-item survey based on the Exercise Benefits and Barriers Scale (13). Items were scored on a 4-point Likert scale, with the response options strongly agree, agree, disagree, and strongly disagree. For each perceived benefit and barrier item, we dichotomized the response: strongly agree and agree were collapsed as one category and disagree and strongly disagree were collapsed as the second category.
Exercise self-efficacy was measured using an instrument developed by Sallis and colleagues (14) to measure self-efficacy for exercise behaviors. This survey instrument measures an individual’s perception that he/she has the ability to exercise in 12 different situations (e.g., when feeling depressed, on weekends, when there are excessive demands at work) using a 5-point Likert-type scale, ranging from “I know I cannot” to “I know I can.” The instrument provides scores for exercise self-efficacy on two scales: sticking to it (adhering to an exercise regime regardless of mood and situation) and making time for exercise (prioritizing exercise over other time demands).
We defined diabetes as fasting capillary blood glucose (CBG) >125 mg·dL−1 or 2-h post glucose CBG value >199 mg·dL−1, impaired fasting glucose (IFG) as fasting CBG between 100 and 125 mg·dL−1. Impaired glucose tolerance (IGT) was defined as 2-h post glucose CBG value between 140 and 199 mg·dL−1, and both IFG and IGT as fasting CBG between 100 and 125 mg·dL−1, 2-h post glucose CBG, value between 140 and 199 mg·dL−1 (15).
The distribution of each variable was determined as normally distributed. Continuous variables are described using mean and SD, whereas categorical data are presented as proportions. We describe overall sociodemographic and economic characteristics, anthropometric measurements, exercise patterns and behaviors, and health characteristics. Student’s t-tests were used to compare means of continuous variables between men and women and between high exercisers and low exercisers. Chi-square tests were used to compare proportions between men and women and between high exercisers and low exercisers. Data were analyzed using the statistical software package SAS version 9.2.
The Emory University Institutional Review Board for the protection of human subjects approved this study. All participants signed informed consent forms before starting the study.
Of the 52 people included in this analysis, 19% (n = 10) reported exercising at least 150 min·wk−1 to meet the U.S. Physical Activity Guidelines (12). Table 1 displays demographic and socioeconomic characteristics in the overall sample by exercise status and sex. Overall, the sample was middle aged (44.6 ± 10.6 yr), predominantly male (65%), and well educated (92% had at least a 4-yr college degree). High exercisers were older (50 vs 43 yr; NS) and had lower incomes (>US$100,000 per year 20% vs 60%; P < 0.05) than low exercisers. Overall, 65% reported preferring walking to other modes of exercise; of the high exercisers, 90% reporting walking as their primary form of exercise (Table 2). Further, they preferred to walk at home or within their neighborhoods (90% among high exercisers and 92% overall) (Table 2). There were no significant differences in BMI or waist and hip circumferences between the groups, the overall mean BMI was 28 kg·m−2, and the overall waist-to-hip ratio was 0.92 (Table 2).
The majority of the sample had never (42 [81%]) or were former (7 [14%]) smokers. Fasting glucose was the only biochemical measure that was significantly different between high exercisers and low exercisers (94.8  vs 110.9 [29.1]; P < 0.01), although all biochemical measurements were better among high exercisers (Table 3).
There were no differences between high exercisers and low exercisers when asked about the benefits of exercise (Table 4). Both groups agreed that exercise was beneficial in most aspects with the exception of “allows contact with family and friends.” There were also no significant differences between barriers to exercise. Men most frequently reported “takes up too much time” (53%), whereas women most often reported “tires me out” (56%). High exercisers’ top barriers to exercise were “takes up too much time” (70%) followed by “tires me out” (60%). By contrast, low exercisers’ top barriers were “tires me out” (60%) followed by “takes up too much time” (43%).
To our knowledge, this is the first description of physical activity patterns, behaviors, and perceptions of South Asians living in the southern United States. Our analysis has concluded that less than 20% of our population meet the U.S. Physical Activity Guidelines. High exercisers in our population tend to choose walking close to home rather than group classes or gyms. Although barriers to exercise were similar between high exercisers and low exercisers, we observed differences between men and women. Men reported lack of time as the primary reason for not exercising, whereas women cited exercise tired them out and their families did not encourage them to exercise. All participants understood the benefits of exercise.
Although no direct comparisons to our study exist, sedentary lifestyles including low physical activity is common among South Asians living in the West (16,17). A review by Bhatnagar et al. (18) found four articles that discussed physical activity patterns in South Asian adults living in the United Kingdom. They found South Asian men and women report exercising less than their Caucasian counterparts, with women reporting the least (18–22). Similar to our findings, most studies find between 50% and 75% of South Asians did not meet the guidelines for physical activity (17,19,23,24). Other reviews have found that physical activity is known to be beneficial for health among South Asians (18,25). However, the definition of “organized” physical activity differed among South Asians, where many believed that prayer and housework were as beneficial. Most studies did not ask specific questions about the definition of physical activity leading to information bias. In addition, motivators and barriers differ between studies. Barriers to exercising reported in the Bhatnagar et al. study not only included a lack of time and tiredness similar to our study but also included lack of facilities close to home and religious restrictions (i.e., dress and mixed gender facilities) (18). Others cited lack of information or direction from healthcare professionals as barriers (26). Lastly, language barriers and traveling outside of their immediate communities for exercise were listed as barriers in other studies (26–28), which may be why our study participants reported more frequently exercising near home.
Health professionals can improve the health of this population by planning future interventions involving South Asians to accommodate these preferences and should be designed with these barriers in mind. Interventions should include separate programs for each gender. Otherwise, programs should incorporate home-based exercise to improve retention. Programs should also include education around positive and negative side effects and the associated benefits of exercise. Community-based walking programs might be an appropriate choice and can help provide accountability for these high-risk, immigrant South Asians.
A strength of this study is the novelty of studying South Asians in the southern United States where obesity, 32% in Georgia, and sedentary behaviors are prevalent (29). However, our small sample size is a limitation. This study was cross sectional, which inhibits any direct causality assumptions. Furthermore, self-reported physical activity minutes per week are often overestimated when compared with objective measure, suggesting that the few categorized as high exercisers in this study may have been overestimated (30).
In conclusion, majority of South Asians living in the southern United States do not meet the U.S. Physical Activity Guidelines. Physical activity interventions implemented by public health workers must consider perceived barriers to exercise for men and women separately and may benefit from tailored programs separated by gender.
This work was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (NIDDK/NIH) under grant numbers R34 DK081723, P30 DK111024, T32 DK007298, and T32 DK007734; the National Center for Advancing Translational Sciences of the National Institutes of Health under award number UL1TR002378. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health; the National Center for Research Resources under grant number UL1 RR025008; the Emory University Coalition of University–.Community Partnerships; and the American Diabetes Association.
None of the authors have any conflicts of interest. The results of the present study do not constitute endorsement by the American College of Sports Medicine.
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